{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Pandas Tutorial for Beginners"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is Pandas?\n",
    "\n",
    "- Pandas is a python package / library \n",
    "- Pandas is a library for data manipulation and analysis\n",
    "- Two main data structures: the Series and DataFrame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SKcfREF8o0m0aLA2GE8c7OzEx7glv3lzYHjUI0Ce+Xqs3zisDAPTdnrEaBK6gqASwAOgZ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lJVd/5k+Nhhh4Ye3buFf/j4x//+z2M5673i9Ko9/mvbf4X/9+c/h7+++ZablBXsK8NPDrPm3S4I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\nMBAyaX9GI6OeqPv4+Lirj6+zu9uTwStDul1NmVPS37oGbBNCV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vjJ5zo9O0cs3aFrH0mBWdUWHnukYAAAAAIKHiKVeXfuIv48KVKVckRUoEUNxMvAIA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\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N2V2FSgAAEHVCVwAAhIwWBACIvk0b3t761MJHPv7Yokf7rVq9NvyZXryILi+V3QSvgIJV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AAAVN2uenozVrwCSACrXgEAAJTXgRf2GjEIAAAAUH2KVwBUV7TqVderhz6QBAAAwNxFIwY3PPKX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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 300
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visual representation of Pandas\n",
    "\n",
    "import os\n",
    "from IPython.display import Image\n",
    "PATH = \"F:\\\\Github\\\\Python tutorials\\\\Introduction to Pandas\\\\\"\n",
    "Image(filename = PATH + \"pandas.png\", width=300, height=300)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial Overview\n",
    "\n",
    "### Video 1\n",
    "1. What is a Panda's Series\n",
    "2. What is a Dataframe\n",
    "3. Creating DataFrames\n",
    "4. Loading a CSV into a DataFrame\n",
    "5. Basic methods for investigating/viewing a DataFrame\n",
    "\n",
    "### Video 2\n",
    "6. Column Filtering\n",
    "7. Row Filtering\n",
    "8. Filtering / Slicing\n",
    "9. Sorting the DF\n",
    "10. Summarising / Aggregating Data\n",
    "11. Creating New Calculated Fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing / Installing packages\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Packages / libraries\n",
    "import os #provides functions for interacting with the operating system\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# To install Pandas type \"pip install pandas\" to the anaconda terminal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. What is Panda's Series\n",
    "\n",
    "Series is a one-dimensional object (similar to a vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         28\n",
       "1       1.79\n",
       "2    Yiannis\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a Series by passing in a list without the Index\n",
    "s = pd.Series(['28', '1.79', 'Yiannis'])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age            28\n",
       "Height    1.79 cm\n",
       "Name      Yiannis\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a Series by passing in a list & the Indexs\n",
    "s = pd.Series(['28', '1.79 cm', 'Yiannis'], index=['Age', 'Height', \"Name\"])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Yiannis'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Indexing / filtering the Series\n",
    "s['Name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. What is a Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame is like an Excel table / PivotTable. It is a tabular data structure comprised of rows and columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Creating a DataFrame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Name, Gender, Age, Height]\n",
       "Index: []"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Manually\n",
    "\n",
    "df = pd.DataFrame(columns = [\"Name\", 'Gender','Age','Height'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Joe</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tom</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tine</td>\n",
       "      <td>Female</td>\n",
       "      <td>93</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Gender Age  Height\n",
       "0   Joe    Male  23    1.70\n",
       "1   Tom    Male  50    1.80\n",
       "2  Tine  Female  93    1.79"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Passing Values in the DataFrame\n",
    "df.loc[0] = [\"Joe\", \"Male\", 23, 1.70]\n",
    "df.loc[1] = [\"Tom\", \"Male\", '50', 1.80]\n",
    "df.loc[2] = [\"Tine\", \"Female\", 93, 1.79]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Joe</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tom</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name Gender Age  Height\n",
       "0  Joe   Male  23     1.7\n",
       "1  Tom   Male  50     1.8"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating DataFrame from a List\n",
    "a_list = [[\"Joe\", \"Male\", 23, 1.70], [\"Tom\", \"Male\", '50', 1.80]]\n",
    "a_list\n",
    "\n",
    "df = pd.DataFrame(a_list, columns = [\"Name\", 'Gender','Age','Height'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2]\n",
      " [ 3  4  5]\n",
      " [ 6  7  8]\n",
      " [ 9 10 11]\n",
      " [12 13 14]]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B   C\n",
       "0   0   1   2\n",
       "1   3   4   5\n",
       "2   6   7   8\n",
       "3   9  10  11\n",
       "4  12  13  14"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating DataFrame from an Array\n",
    "a = np.arange(15).reshape(5,3)\n",
    "print(a)\n",
    "\n",
    "df = pd.DataFrame(a, columns = [\"A\", 'B','C'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1  2   3   4\n",
       "A  0  3  6   9  12\n",
       "B  1  4  7  10  13\n",
       "C  2  5  8  11  14"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose a DataFrame\n",
    "\n",
    "#method 1\n",
    "df.T\n",
    "\n",
    "# method 2\n",
    "np.transpose(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Loading a CSV file into a DataFrame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\pitsi\\\\Desktop\\\\Python Tutorials'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is to find out your current directory\n",
    "cwd = os.getcwd()\n",
    "cwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>707</td>\n",
       "      <td>5211</td>\n",
       "      <td>651.375</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/11/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1455</td>\n",
       "      <td>10386</td>\n",
       "      <td>1298.250</td>\n",
       "      <td>Promotion Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/11/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1520</td>\n",
       "      <td>12475</td>\n",
       "      <td>1559.375</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/11/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1726</td>\n",
       "      <td>14414</td>\n",
       "      <td>1801.750</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13/11/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2134</td>\n",
       "      <td>20916</td>\n",
       "      <td>2614.500</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Day_Name  Visitors  Revenue  Marketing Spend           Promo\n",
       "0  09/11/2020     Monday       707     5211          651.375        No Promo\n",
       "1  10/11/2020    Tuesday      1455    10386         1298.250   Promotion Red\n",
       "2  11/11/2020  Wednesday      1520    12475         1559.375  Promotion Blue\n",
       "3  12/11/2020   Thursday      1726    14414         1801.750        No Promo\n",
       "4  13/11/2020     Friday      2134    20916         2614.500        No Promo"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the data\n",
    "\n",
    "raw_data = pd.read_csv('F:\\\\Github\\Python tutorials\\\\Introduction to Pandas\\\\Marketing Raw Data.csv')\n",
    "\n",
    "# runs all the data\n",
    "raw_data\n",
    "\n",
    "#runs the first 5 rows\n",
    "raw_data.head(1)\n",
    "\n",
    "#runs the number of rows you select\n",
    "raw_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Basic methods for investigating/viewing a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>05/05/2021</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1400</td>\n",
       "      <td>11196</td>\n",
       "      <td>1119.600000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>06/05/2021</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2244</td>\n",
       "      <td>18611</td>\n",
       "      <td>2067.888889</td>\n",
       "      <td>Promotion Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>07/05/2021</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2023</td>\n",
       "      <td>14502</td>\n",
       "      <td>1450.200000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>08/05/2021</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1483</td>\n",
       "      <td>8975</td>\n",
       "      <td>1121.875000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>09/05/2021</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1303</td>\n",
       "      <td>6968</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date   Day_Name  Visitors  Revenue  Marketing Spend          Promo\n",
       "177  05/05/2021  Wednesday      1400    11196      1119.600000       No Promo\n",
       "178  06/05/2021   Thursday      2244    18611      2067.888889  Promotion Red\n",
       "179  07/05/2021     Friday      2023    14502      1450.200000       No Promo\n",
       "180  08/05/2021   Saturday      1483     8975      1121.875000       No Promo\n",
       "181  09/05/2021     Sunday      1303     6968       871.000000       No Promo"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the last N number of rows\n",
    "raw_data.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(182, 6)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape\n",
    "raw_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 182 entries, 0 to 181\n",
      "Data columns (total 6 columns):\n",
      "Date               182 non-null object\n",
      "Day_Name           182 non-null object\n",
      "Visitors           182 non-null int64\n",
      "Revenue            182 non-null int64\n",
      "Marketing Spend    182 non-null float64\n",
      "Promo              182 non-null object\n",
      "dtypes: float64(1), int64(2), object(3)\n",
      "memory usage: 8.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Displays the information for our DF\n",
    "raw_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 182 entries, 0 to 181\n",
      "Data columns (total 6 columns):\n",
      "Date               182 non-null datetime64[ns]\n",
      "Day_Name           182 non-null object\n",
      "Visitors           182 non-null int64\n",
      "Revenue            182 non-null int64\n",
      "Marketing Spend    182 non-null float64\n",
      "Promo              182 non-null object\n",
      "dtypes: datetime64[ns](1), float64(1), int64(2), object(2)\n",
      "memory usage: 8.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Converting Date to Date\n",
    "raw_data['Date'] = pd.to_datetime(raw_data['Date'], format='%d/%m/%Y')\n",
    "raw_data\n",
    "raw_data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>182.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>182.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1666.730769</td>\n",
       "      <td>12991.840659</td>\n",
       "      <td>1396.356564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>503.528049</td>\n",
       "      <td>5883.117597</td>\n",
       "      <td>691.867416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>488.000000</td>\n",
       "      <td>2898.000000</td>\n",
       "      <td>322.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1339.000000</td>\n",
       "      <td>8808.500000</td>\n",
       "      <td>880.431250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1546.000000</td>\n",
       "      <td>11547.500000</td>\n",
       "      <td>1223.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2027.500000</td>\n",
       "      <td>15816.500000</td>\n",
       "      <td>1676.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4139.000000</td>\n",
       "      <td>36283.000000</td>\n",
       "      <td>4535.375000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Visitors       Revenue  Marketing Spend\n",
       "count   182.000000    182.000000       182.000000\n",
       "mean   1666.730769  12991.840659      1396.356564\n",
       "std     503.528049   5883.117597       691.867416\n",
       "min     488.000000   2898.000000       322.000000\n",
       "25%    1339.000000   8808.500000       880.431250\n",
       "50%    1546.000000  11547.500000      1223.900000\n",
       "75%    2027.500000  15816.500000      1676.450000\n",
       "max    4139.000000  36283.000000      4535.375000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displays the summary statistics for all numeric columns\n",
    "raw_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Column Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     707\n",
       "1    1455\n",
       "2    1520\n",
       "3    1726\n",
       "4    2134\n",
       "Name: Visitors, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exmp 1 - Series\n",
    "raw_data['Visitors'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     707\n",
       "1    1455\n",
       "2    1520\n",
       "3    1726\n",
       "4    2134\n",
       "Name: Visitors, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exmp 2 - Series\n",
    "raw_data.Visitors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>707</td>\n",
       "      <td>5211</td>\n",
       "      <td>651.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1455</td>\n",
       "      <td>10386</td>\n",
       "      <td>1298.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1520</td>\n",
       "      <td>12475</td>\n",
       "      <td>1559.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1726</td>\n",
       "      <td>14414</td>\n",
       "      <td>1801.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2134</td>\n",
       "      <td>20916</td>\n",
       "      <td>2614.500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Visitors  Revenue  Marketing Spend\n",
       "0       707     5211          651.375\n",
       "1      1455    10386         1298.250\n",
       "2      1520    12475         1559.375\n",
       "3      1726    14414         1801.750\n",
       "4      2134    20916         2614.500"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exmp 3 -  Data Frame\n",
    "type(raw_data[['Visitors']])\n",
    "\n",
    "# Exmp 4 -  Data Frame 2 columns +\n",
    "raw_data[['Visitors', 'Revenue','Marketing Spend']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Visitors    303345\n",
       "dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating an Array from a df column\n",
    "\n",
    "# exm 1\n",
    "np.array(raw_data[['Visitors']])\n",
    "\n",
    "# exm 2\n",
    "raw_data[['Visitors']].values\n",
    "\n",
    "raw_data[['Visitors']].sum()\n",
    "\n",
    "np.sum(raw_data[['Visitors']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Row Filtering\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>707</td>\n",
       "      <td>5211</td>\n",
       "      <td>651.375</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/11/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1455</td>\n",
       "      <td>10386</td>\n",
       "      <td>1298.250</td>\n",
       "      <td>Promotion Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/11/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1520</td>\n",
       "      <td>12475</td>\n",
       "      <td>1559.375</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/11/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1726</td>\n",
       "      <td>14414</td>\n",
       "      <td>1801.750</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13/11/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2134</td>\n",
       "      <td>20916</td>\n",
       "      <td>2614.500</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Day_Name  Visitors  Revenue  Marketing Spend           Promo\n",
       "0  09/11/2020     Monday       707     5211          651.375        No Promo\n",
       "1  10/11/2020    Tuesday      1455    10386         1298.250   Promotion Red\n",
       "2  11/11/2020  Wednesday      1520    12475         1559.375  Promotion Blue\n",
       "3  12/11/2020   Thursday      1726    14414         1801.750        No Promo\n",
       "4  13/11/2020     Friday      2134    20916         2614.500        No Promo"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exmp 1\n",
    "raw_data[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>707</td>\n",
       "      <td>5211</td>\n",
       "      <td>651.375</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/11/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1455</td>\n",
       "      <td>10386</td>\n",
       "      <td>1298.250</td>\n",
       "      <td>Promotion Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/11/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1520</td>\n",
       "      <td>12475</td>\n",
       "      <td>1559.375</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/11/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1726</td>\n",
       "      <td>14414</td>\n",
       "      <td>1801.750</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13/11/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2134</td>\n",
       "      <td>20916</td>\n",
       "      <td>2614.500</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14/11/2020</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1316</td>\n",
       "      <td>12996</td>\n",
       "      <td>1444.000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Day_Name  Visitors  Revenue  Marketing Spend           Promo\n",
       "0  09/11/2020     Monday       707     5211          651.375        No Promo\n",
       "1  10/11/2020    Tuesday      1455    10386         1298.250   Promotion Red\n",
       "2  11/11/2020  Wednesday      1520    12475         1559.375  Promotion Blue\n",
       "3  12/11/2020   Thursday      1726    14414         1801.750        No Promo\n",
       "4  13/11/2020     Friday      2134    20916         2614.500        No Promo\n",
       "5  14/11/2020   Saturday      1316    12996         1444.000  Promotion Blue"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exmp 2\n",
    "raw_data.loc[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Filtering / Slicing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>707</td>\n",
       "      <td>5211</td>\n",
       "      <td>651.375000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1548</td>\n",
       "      <td>10072</td>\n",
       "      <td>1119.111111</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>23/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2632</td>\n",
       "      <td>34278</td>\n",
       "      <td>4284.750000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>30/11/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1541</td>\n",
       "      <td>9144</td>\n",
       "      <td>1016.000000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>07/12/2020</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1584</td>\n",
       "      <td>9612</td>\n",
       "      <td>961.200000</td>\n",
       "      <td>Promotion Red</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Day_Name  Visitors  Revenue  Marketing Spend           Promo\n",
       "0   09/11/2020   Monday       707     5211       651.375000        No Promo\n",
       "7   16/11/2020   Monday      1548    10072      1119.111111        No Promo\n",
       "14  23/11/2020   Monday      2632    34278      4284.750000  Promotion Blue\n",
       "21  30/11/2020   Monday      1541     9144      1016.000000        No Promo\n",
       "28  07/12/2020   Monday      1584     9612       961.200000   Promotion Red"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select all the data when the day is Monday\n",
    "\n",
    "# exmp 1\n",
    "raw_data[raw_data['Day_Name'] == 'Monday']\n",
    "\n",
    "# exmp 2\n",
    "raw_data[raw_data.Day_Name == 'Monday'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/11/2020</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1455</td>\n",
       "      <td>10386</td>\n",
       "      <td>1298.250</td>\n",
       "      <td>Promotion Red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/11/2020</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1520</td>\n",
       "      <td>12475</td>\n",
       "      <td>1559.375</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/11/2020</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1726</td>\n",
       "      <td>14414</td>\n",
       "      <td>1801.750</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13/11/2020</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2134</td>\n",
       "      <td>20916</td>\n",
       "      <td>2614.500</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14/11/2020</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1316</td>\n",
       "      <td>12996</td>\n",
       "      <td>1444.000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Day_Name  Visitors  Revenue  Marketing Spend           Promo\n",
       "1  10/11/2020    Tuesday      1455    10386         1298.250   Promotion Red\n",
       "2  11/11/2020  Wednesday      1520    12475         1559.375  Promotion Blue\n",
       "3  12/11/2020   Thursday      1726    14414         1801.750        No Promo\n",
       "4  13/11/2020     Friday      2134    20916         2614.500        No Promo\n",
       "5  14/11/2020   Saturday      1316    12996         1444.000  Promotion Blue"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select all the data when the day is Marketing Spend > 1000\n",
    "\n",
    "# exmp 1\n",
    "raw_data[raw_data['Marketing Spend'] > 1000].head()\n",
    "\n",
    "#raw_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-11-12</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>1726</td>\n",
       "      <td>14414</td>\n",
       "      <td>1801.750000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-11-13</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2134</td>\n",
       "      <td>20916</td>\n",
       "      <td>2614.500000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-11-16</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1548</td>\n",
       "      <td>10072</td>\n",
       "      <td>1119.111111</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-11-19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2321</td>\n",
       "      <td>17660</td>\n",
       "      <td>1605.454545</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1541</td>\n",
       "      <td>9144</td>\n",
       "      <td>1016.000000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Day_Name  Visitors  Revenue  Marketing Spend     Promo\n",
       "3  2020-11-12  Thursday      1726    14414      1801.750000  No Promo\n",
       "4  2020-11-13    Friday      2134    20916      2614.500000  No Promo\n",
       "7  2020-11-16    Monday      1548    10072      1119.111111  No Promo\n",
       "10 2020-11-19  Thursday      2321    17660      1605.454545  No Promo\n",
       "21 2020-11-30    Monday      1541     9144      1016.000000  No Promo"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select all the data when the day is Marketing Spend > 1000 & Promo = \"No Promo\"\n",
    "\n",
    "# exmp 1 - 2 conditions\n",
    "raw_data[(raw_data['Marketing Spend'] > 1000) & (raw_data['Promo'] == 'No Promo')].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Sorting the DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2020-12-26</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2678</td>\n",
       "      <td>36283</td>\n",
       "      <td>4535.375000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-11-23</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2632</td>\n",
       "      <td>34278</td>\n",
       "      <td>4284.750000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>4139</td>\n",
       "      <td>30146</td>\n",
       "      <td>3014.600000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2021-03-13</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2732</td>\n",
       "      <td>28196</td>\n",
       "      <td>3524.500000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2553</td>\n",
       "      <td>26608</td>\n",
       "      <td>2418.909091</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Day_Name  Visitors  Revenue  Marketing Spend           Promo\n",
       "47  2020-12-26  Saturday      2678    36283      4535.375000  Promotion Blue\n",
       "14  2020-11-23    Monday      2632    34278      4284.750000  Promotion Blue\n",
       "59  2021-01-07  Thursday      4139    30146      3014.600000        No Promo\n",
       "124 2021-03-13  Saturday      2732    28196      3524.500000  Promotion Blue\n",
       "109 2021-02-26    Friday      2553    26608      2418.909091  Promotion Blue"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exmp 1 - by Date\n",
    "raw_data.sort_values(by = 'Date')\n",
    "\n",
    "# Exmp 2 - passing it back v1\n",
    "raw_data = raw_data.sort_values(by = 'Date')\n",
    "\n",
    "# Exmp 3 - passing it back v2\n",
    "raw_data.sort_values(by = 'Date', inplace = True)\n",
    "\n",
    "# Exmp 4 - by Rev asc\n",
    "raw_data.sort_values(by = 'Revenue')\n",
    "\n",
    "# Exmp 5 - by Rev desc\n",
    "raw_data.sort_values(by = 'Revenue', ascending = False, inplace = True)\n",
    "\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Aggregating the DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "322.0"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exmp 1 - Total Revenue\n",
    "raw_data['Revenue'].sum()\n",
    "np.sum(raw_data['Revenue'])\n",
    "\n",
    "# Exmp 2 - Average Visitors\n",
    "raw_data['Visitors'].mean()\n",
    "\n",
    "# Exmp 3 - Max Marketing Spend\n",
    "raw_data['Marketing Spend'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Friday</td>\n",
       "      <td>447823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>270080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>285171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>286458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>491323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>261206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>322454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Day_Name  Revenue\n",
       "0     Friday   447823\n",
       "1     Monday   270080\n",
       "2   Saturday   285171\n",
       "3     Sunday   286458\n",
       "4   Thursday   491323\n",
       "5    Tuesday   261206\n",
       "6  Wednesday   322454"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum of Revenue by Day Name\n",
    "\n",
    "# option 1 - Series\n",
    "raw_data.groupby('Day_Name')['Revenue'].sum()\n",
    "\n",
    "# option 2 - df\n",
    "a = raw_data.groupby('Day_Name', as_index = False).agg({'Revenue':'sum'})\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sum of Revenue by Day Name & number of Data points\n",
    "a = raw_data.groupby('Day_Name', as_index = False).agg({'Revenue':'sum', 'Date':'count'})\n",
    "\n",
    "# Renaming columns v1\n",
    "#a.columns = ['Day_Name','Revenue','Count of Datapoints']\n",
    "#a\n",
    "\n",
    "# Renaming columns v1\n",
    "a.columns = a.columns.str.replace('Date', 'Count of Datapoints')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Day_Name', 'Visitors', 'Revenue', 'Marketing Spend', 'Promo'], dtype='object')"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Promo</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Marketing Spend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Promo</td>\n",
       "      <td>10945.229730</td>\n",
       "      <td>1627.648649</td>\n",
       "      <td>1176.664264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>16754.185185</td>\n",
       "      <td>1790.907407</td>\n",
       "      <td>1829.731425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Promotion Red</td>\n",
       "      <td>12034.111111</td>\n",
       "      <td>1596.111111</td>\n",
       "      <td>1264.041522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Promo       Revenue     Visitors  Marketing Spend\n",
       "0        No Promo  10945.229730  1627.648649      1176.664264\n",
       "1  Promotion Blue  16754.185185  1790.907407      1829.731425\n",
       "2   Promotion Red  12034.111111  1596.111111      1264.041522"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# avg Summary by Promo\n",
    "raw_data.groupby('Promo', as_index = False).agg({'Revenue':'mean', 'Visitors':'mean', 'Marketing Spend':'mean'})\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Promo</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Marketing Spend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Promo</td>\n",
       "      <td>8128.000000</td>\n",
       "      <td>1302.818182</td>\n",
       "      <td>839.546511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>16429.000000</td>\n",
       "      <td>1666.125000</td>\n",
       "      <td>1940.668056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Promotion Red</td>\n",
       "      <td>9190.142857</td>\n",
       "      <td>1285.857143</td>\n",
       "      <td>953.681061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Promo       Revenue     Visitors  Marketing Spend\n",
       "0        No Promo   8128.000000  1302.818182       839.546511\n",
       "1  Promotion Blue  16429.000000  1666.125000      1940.668056\n",
       "2   Promotion Red   9190.142857  1285.857143       953.681061"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum the revenue by Promo when the Day Name = 'Saturday'\n",
    "raw_data[raw_data['Day_Name'] == 'Saturday'].groupby('Promo', as_index = False).agg({'Revenue':'mean', 'Visitors':'mean', 'Marketing Spend':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2020-12-26</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2678</td>\n",
       "      <td>36283</td>\n",
       "      <td>4535.375000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-11-23</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2632</td>\n",
       "      <td>34278</td>\n",
       "      <td>4284.750000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>4139</td>\n",
       "      <td>30146</td>\n",
       "      <td>3014.600000</td>\n",
       "      <td>No Promo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2021-03-13</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2732</td>\n",
       "      <td>28196</td>\n",
       "      <td>3524.500000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2553</td>\n",
       "      <td>26608</td>\n",
       "      <td>2418.909091</td>\n",
       "      <td>Promotion Blue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Day_Name  Visitors  Revenue  Marketing Spend           Promo\n",
       "47  2020-12-26  Saturday      2678    36283      4535.375000  Promotion Blue\n",
       "14  2020-11-23    Monday      2632    34278      4284.750000  Promotion Blue\n",
       "59  2021-01-07  Thursday      4139    30146      3014.600000        No Promo\n",
       "124 2021-03-13  Saturday      2732    28196      3524.500000  Promotion Blue\n",
       "109 2021-02-26    Friday      2553    26608      2418.909091  Promotion Blue"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11. Creating New Calculated Fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "      <th>Revenue per Visitor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2020-12-26</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2678</td>\n",
       "      <td>36283</td>\n",
       "      <td>4535.375000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>13.548544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-11-23</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2632</td>\n",
       "      <td>34278</td>\n",
       "      <td>4284.750000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>13.023556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>4139</td>\n",
       "      <td>30146</td>\n",
       "      <td>3014.600000</td>\n",
       "      <td>No Promo</td>\n",
       "      <td>7.283402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2021-03-13</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2732</td>\n",
       "      <td>28196</td>\n",
       "      <td>3524.500000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>10.320644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2553</td>\n",
       "      <td>26608</td>\n",
       "      <td>2418.909091</td>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>10.422248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Day_Name  Visitors  Revenue  Marketing Spend           Promo  \\\n",
       "47  2020-12-26  Saturday      2678    36283      4535.375000  Promotion Blue   \n",
       "14  2020-11-23    Monday      2632    34278      4284.750000  Promotion Blue   \n",
       "59  2021-01-07  Thursday      4139    30146      3014.600000        No Promo   \n",
       "124 2021-03-13  Saturday      2732    28196      3524.500000  Promotion Blue   \n",
       "109 2021-02-26    Friday      2553    26608      2418.909091  Promotion Blue   \n",
       "\n",
       "     Revenue per Visitor  \n",
       "47             13.548544  \n",
       "14             13.023556  \n",
       "59              7.283402  \n",
       "124            10.320644  \n",
       "109            10.422248  "
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revenue per Visitor\n",
    "raw_data['Revenue per Visitor'] = raw_data['Revenue'] / raw_data['Visitors']\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "      <th>Revenue per Visitor</th>\n",
       "      <th>Revenue per Spend</th>\n",
       "      <th>Aasdfsad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2020-12-26</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2678</td>\n",
       "      <td>36283</td>\n",
       "      <td>4535.375000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>13.548544</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-11-23</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2632</td>\n",
       "      <td>34278</td>\n",
       "      <td>4284.750000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>13.023556</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>4139</td>\n",
       "      <td>30146</td>\n",
       "      <td>3014.600000</td>\n",
       "      <td>No Promo</td>\n",
       "      <td>7.283402</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2021-03-13</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2732</td>\n",
       "      <td>28196</td>\n",
       "      <td>3524.500000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>10.320644</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2553</td>\n",
       "      <td>26608</td>\n",
       "      <td>2418.909091</td>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>10.422248</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Day_Name  Visitors  Revenue  Marketing Spend           Promo  \\\n",
       "47  2020-12-26  Saturday      2678    36283      4535.375000  Promotion Blue   \n",
       "14  2020-11-23    Monday      2632    34278      4284.750000  Promotion Blue   \n",
       "59  2021-01-07  Thursday      4139    30146      3014.600000        No Promo   \n",
       "124 2021-03-13  Saturday      2732    28196      3524.500000  Promotion Blue   \n",
       "109 2021-02-26    Friday      2553    26608      2418.909091  Promotion Blue   \n",
       "\n",
       "     Revenue per Visitor  Revenue per Spend  Aasdfsad  \n",
       "47             13.548544                8.0       8.0  \n",
       "14             13.023556                8.0       8.0  \n",
       "59              7.283402               10.0      10.0  \n",
       "124            10.320644                8.0       8.0  \n",
       "109            10.422248               11.0      11.0  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revenue per Spend\n",
    "raw_data['Revenue per Spend'] = raw_data['Revenue'] / raw_data['Marketing Spend']\n",
    "raw_data.head()\n",
    "\n",
    "raw_data['Aasdfsad'] = raw_data['Revenue'] / raw_data['Marketing Spend']\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "      <th>Revenue per Visitor</th>\n",
       "      <th>Revenue per Spend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2020-12-26</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2678</td>\n",
       "      <td>36283</td>\n",
       "      <td>4535.375000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>13.548544</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-11-23</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2632</td>\n",
       "      <td>34278</td>\n",
       "      <td>4284.750000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>13.023556</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>4139</td>\n",
       "      <td>30146</td>\n",
       "      <td>3014.600000</td>\n",
       "      <td>No Promo</td>\n",
       "      <td>7.283402</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2021-03-13</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2732</td>\n",
       "      <td>28196</td>\n",
       "      <td>3524.500000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>10.320644</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2553</td>\n",
       "      <td>26608</td>\n",
       "      <td>2418.909091</td>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>10.422248</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Day_Name  Visitors  Revenue  Marketing Spend           Promo  \\\n",
       "47  2020-12-26  Saturday      2678    36283      4535.375000  Promotion Blue   \n",
       "14  2020-11-23    Monday      2632    34278      4284.750000  Promotion Blue   \n",
       "59  2021-01-07  Thursday      4139    30146      3014.600000        No Promo   \n",
       "124 2021-03-13  Saturday      2732    28196      3524.500000  Promotion Blue   \n",
       "109 2021-02-26    Friday      2553    26608      2418.909091  Promotion Blue   \n",
       "\n",
       "     Revenue per Visitor  Revenue per Spend  \n",
       "47             13.548544                8.0  \n",
       "14             13.023556                8.0  \n",
       "59              7.283402               10.0  \n",
       "124            10.320644                8.0  \n",
       "109            10.422248               11.0  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Deleting a column\n",
    "del raw_data['Aasdfsad']\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_Name</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Promo</th>\n",
       "      <th>Revenue per Visitor</th>\n",
       "      <th>Revenue per Spend</th>\n",
       "      <th>Spend per visitor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2020-12-26</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2678</td>\n",
       "      <td>36283</td>\n",
       "      <td>4535.375000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>13.548544</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.693568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-11-23</td>\n",
       "      <td>Monday</td>\n",
       "      <td>2632</td>\n",
       "      <td>34278</td>\n",
       "      <td>4284.750000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>13.023556</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.627945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>4139</td>\n",
       "      <td>30146</td>\n",
       "      <td>3014.600000</td>\n",
       "      <td>No Promo</td>\n",
       "      <td>7.283402</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.728340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2021-03-13</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>2732</td>\n",
       "      <td>28196</td>\n",
       "      <td>3524.500000</td>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>10.320644</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.290081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>Friday</td>\n",
       "      <td>2553</td>\n",
       "      <td>26608</td>\n",
       "      <td>2418.909091</td>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>10.422248</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.947477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Day_Name  Visitors  Revenue  Marketing Spend           Promo  \\\n",
       "47  2020-12-26  Saturday      2678    36283      4535.375000  Promotion Blue   \n",
       "14  2020-11-23    Monday      2632    34278      4284.750000  Promotion Blue   \n",
       "59  2021-01-07  Thursday      4139    30146      3014.600000        No Promo   \n",
       "124 2021-03-13  Saturday      2732    28196      3524.500000  Promotion Blue   \n",
       "109 2021-02-26    Friday      2553    26608      2418.909091  Promotion Blue   \n",
       "\n",
       "     Revenue per Visitor  Revenue per Spend  Spend per visitor  \n",
       "47             13.548544                8.0           1.693568  \n",
       "14             13.023556                8.0           1.627945  \n",
       "59              7.283402               10.0           0.728340  \n",
       "124            10.320644                8.0           1.290081  \n",
       "109            10.422248               11.0           0.947477  "
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Spend per visitor\n",
    "raw_data['Spend per visitor'] = raw_data['Marketing Spend'] / raw_data['Visitors']\n",
    "\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Promo</th>\n",
       "      <th>Revenue per Spend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Promo</td>\n",
       "      <td>694.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Promotion Red</td>\n",
       "      <td>526.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Promo  Revenue per Spend\n",
       "0        No Promo              694.0\n",
       "1  Promotion Blue              504.0\n",
       "2   Promotion Red              526.0"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.groupby('Promo', as_index = False).agg({'Revenue per Spend':'sum'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Promo</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Revenue per Spend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Promo</td>\n",
       "      <td>809947</td>\n",
       "      <td>87073.155556</td>\n",
       "      <td>9.301914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Promotion Blue</td>\n",
       "      <td>904726</td>\n",
       "      <td>98805.496969</td>\n",
       "      <td>9.156636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Promotion Red</td>\n",
       "      <td>649842</td>\n",
       "      <td>68258.242173</td>\n",
       "      <td>9.520345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Promo  Revenue  Marketing Spend  Revenue per Spend\n",
       "0        No Promo   809947     87073.155556           9.301914\n",
       "1  Promotion Blue   904726     98805.496969           9.156636\n",
       "2   Promotion Red   649842     68258.242173           9.520345"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = raw_data.groupby('Promo', as_index = False).agg({'Revenue':'sum', 'Marketing Spend':'sum'})\n",
    "a\n",
    "\n",
    "a['Revenue per Spend'] = a['Revenue'] / a['Marketing Spend']\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": null,
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    "collapsed": true
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   "source": []
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   "execution_count": null,
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    "collapsed": true
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   "outputs": [],
   "source": []
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More Details here: https://pandas.pydata.org/pandas-docs/stable/getting_started/dsintro.html"
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